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Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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Data-efficient Bayesian learning for radial dynamic MR reconstruction.

Sherine Brahma1,2, Christoph Kolbitsch1,3, Joerg Martin1

  • 1Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.

Medical Physics
|June 27, 2023
PubMed
Summary
This summary is machine-generated.

Physics-informed deep learning (DL) effectively quantifies uncertainties in cardiac MRI reconstruction, improving image quality and differentiating artifacts from pathologies. This approach enhances diagnostic accuracy by providing reliable uncertainty metrics for accelerated dynamic imaging.

Failed At:

2026-06-19T13:49:14.691353+00:00

Keywords:
Deep Learningcine MRIuncertainty quantification

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